Abstract

This study focuses on a third-party perspective of idiosyncratic deals (i-deals). More specifically, we look into the differential judgments co-workers make about i-deals in their work environment, as well as their reactions. Based on equity theory, we examine to what extent the content of the i-deal and the work context (i.e., the functional dependence between co-worker and i-dealer) explain co-worker judgments regarding i-deal fairness in addition to subsequent voice behavior (i.e., complaining and/or requesting compensation). A vignette study with 1988 respondents shows that when i-deals are considered distributively unfair, co-workers try to restore equity through voice behavior, thereby making the i-deal less effective. Furthermore, i-deals spark more distributive injustice perceptions and voice behavior in a highly interdependent work context. Finally, on average, financial bonuses were considered most distributively unfair and, thus, trigger more voice behavior. These results have important implications for i-deal literature as they uncover the criteria that co-workers use to judge i-deals and shape their reactions.

Notes

Acknowledgements

Appendix 1

You are working, together with a number of colleagues, for the same supervisor (boss). Recently, one of your colleagues has struck a deal with your supervisor about an exceptional arrangement. This arrangement only applies to this particular colleague, not to you, nor to any other team member

Scenario introduction, common to all versions

Either

The arrangement implies that from now on your colleague can freely choose his or her working hours. However late your colleague arrives or early he or she leaves, your supervisor approves as long as your colleague works a normal amount of hours on a monthly basis

Manipulation of content—Flexibility

or

The arrangement implies that your colleague can take it easy for a while. He or she will work the same amount of hours as before, but his or her workload will be reduced. As long as the arrangement holds, your colleague will get fewer assignments

Manipulation of content—Workload reduction

or

The arrangement implies that your colleague will receive a bonus at the end of each month, provided he or she performs exceptionally well. You, and your colleagues, keep working for a fixed wage. Your colleague can thus improve financially, but has to work hard for it

Manipulation of content—Bonus

Either

The work that your colleague does has no impact on your own work. Your work results do not depend on how well your colleague performs. In the past, when your colleague was absent for a while, your work was not disrupted or hindered

Manipulation of lowfunctional dependence

or

The work that your colleague does has a large impact on your work. Your results are strongly dependent on how well your colleague performs. In the past, when your colleague was absent due to sickness, your work was immediately disrupted or hindered

Manipulation of highfunctional dependence

Your supervisor announced this arrangement last week during a team meeting, where everyone was offered the opportunity to ask questions

Context information, common to all versions of the scenarios

Appendix 2

Robustness checks. a Control variables added to the main model: age, gender, and educational level. Only significant results are represented for the control variables. All control variables were regressed on distributive injustice, complaining, and requesting compensation. 1Reference category: master degree. *p < .05; ***p < .001. b Likelihood of the hypothetical situation added as a control variable to the main model. ***p < .001. c Having an i-deal of their own (yes/no) added as a control variable to the main model. **p < .01; ***p < .001. d Two-group model (group 1 respondents have an i-deal of their own; group 2 respondents have no i-deal of their own). Coefficients group 1/Coefficients group 2. Differences between coefficients are calculated through Wald tests of parameter equalities. *p < .05; **p < .01; ***p < .001. e Pay satisfaction added as a control variable to the main model. **p < .01; ***p < .001

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